






"""simple docstring"""







import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


a					:							int    =						logging.getLogger(__name__)
a					:							List[Any]    =						list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a					:							Optional[int]    =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class        __UpperCAmelCase:




						"""simple docstring"""




						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={
						        "help": (
						            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
						        )
						    }    ,    )
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCAmelCase__				)}    ,    )
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={
						        "help": (
						            "Override some existing default config settings when a model is trained from scratch. Example: "
						            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
						        )
						    }    ,    )
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}				)
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,    )
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}    ,    )
						__lowerCamelCase										=   field(
						    default="main"    ,    metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,    )
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={
						        "help": (
						            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
						            "with private models)."
						        )
						    }    ,    )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''



												if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
																		raise ValueError(
																		    "--config_overrides can\'t be used in combination with --config_name or --model_name_or_path"     )


@dataclass
class        __UpperCAmelCase:




						"""simple docstring"""




						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "The name of the dataset to use (via the datasets library)."}				)
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
						__lowerCamelCase										=   field(default=UpperCAmelCase__    ,    metadata={"help": "The input training data file (a text file)."}				)
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}    ,    )
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}    ,    )
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}    ,    )
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "Overwrite the cached training and evaluation sets"}				)
						__lowerCamelCase										=   field(
						    default=5    ,    metadata={
						        "help": "The percentage of the train set used as validation set in case there's no validation split"
						    }    ,    )
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={
						        "help": (
						            "The maximum total input sequence length after tokenization. Sequences longer "
						            "than this will be truncated. Default to the max input length of the model."
						        )
						    }    ,    )
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={"help": "The number of processes to use for the preprocessing."}    ,    )
						__lowerCamelCase										=   field(
						    default=0.15    ,    metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}				)
						__lowerCamelCase										=   field(
						    default=UpperCAmelCase__    ,    metadata={
						        "help": (
						            "Whether to pad all samples to `max_seq_length`. "
						            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
						        )
						    }    ,    )




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''



												if self.train_file is not None:
																		lowercase__							: List[Any]=			self.train_file.split("."     )[-1]
																		assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
												if self.validation_file is not None:
																		lowercase__							: Optional[Any]=			self.validation_file.split("."     )[-1]
																		assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def 		lowercase__(A   ,						A		)							->Optional[Any]:




						"""simple docstring"""



						with open(UpperCamelCase__   ,						"r"   ,						encoding="utf-8"		) as f:
												lowercase__							: Any=			[json.loads(UpperCamelCase__		) for line in f.read().splitlines() if (len(UpperCamelCase__		) > 0 and not line.isspace())]
						assert len(UpperCamelCase__		) == len(UpperCamelCase__		)

						lowercase__							: Any=			{c: dataset[c] for c in dataset.column_names}
						lowercase__							: Any=			refs
						return Dataset.from_dict(UpperCamelCase__		)





def 		lowercase__()							->List[str]:




						"""simple docstring"""



						lowercase__							: int=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
						if len(sys.argv		) == 2 and sys.argv[1].endswith(".json"		):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowercase__,      lowercase__,      lowercase__							: List[Any]=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
						else:
												lowercase__,      lowercase__,      lowercase__							: Dict=			parser.parse_args_into_dataclasses()

						# Detecting last checkpoint.
						lowercase__							: List[Any]=			None
						if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
												lowercase__							: Any=			get_last_checkpoint(training_args.output_dir		)
												if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
																		raise ValueError(
																		    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																		    "Use --overwrite_output_dir to overcome."		)
												elif last_checkpoint is not None:
																		logger.info(
																		    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																		    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."		)

    # Setup logging
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"   ,						datefmt="%m/%d/%Y %H:%M:%S"   ,						handlers=[logging.StreamHandler(sys.stdout		)]   ,						)
						logger.setLevel(logging.INFO if is_main_process(training_args.local_rank		) else logging.WARN		)

						# Log on each process the small summary:
						logger.warning(
						    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
						    + f'''distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {training_args.fpaa}'''		)
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank		):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info("Training/evaluation parameters %s"   ,						UpperCamelCase__		)

						# Set seed before initializing model.
						set_seed(training_args.seed		)

						# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
						# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
						# (the dataset will be downloaded automatically from the datasets Hub).
						#
						# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
						# 'text' is found. You can easily tweak this behavior (see below).
						#
						# In distributed training, the load_dataset function guarantee that only one local process can concurrently
						# download the dataset.
						if data_args.dataset_name is not None:
												# Downloading and loading a dataset from the hub.
												lowercase__							: Any=			load_dataset(data_args.dataset_name   ,						data_args.dataset_config_name		)
												if "validation" not in datasets.keys():
																		lowercase__							: List[str]=			load_dataset(
																		    data_args.dataset_name   ,						data_args.dataset_config_name   ,						split=f'''train[:{data_args.validation_split_percentage}%]'''   ,						)
																		lowercase__							: Dict=			load_dataset(
																		    data_args.dataset_name   ,						data_args.dataset_config_name   ,						split=f'''train[{data_args.validation_split_percentage}%:]'''   ,						)
						else:
												lowercase__							: Optional[Any]=			{}
												if data_args.train_file is not None:
																		lowercase__							: Tuple=			data_args.train_file
												if data_args.validation_file is not None:
																		lowercase__							: int=			data_args.validation_file
												lowercase__							: Optional[Any]=			data_args.train_file.split("."		)[-1]
												if extension == "txt":
																		lowercase__							: str=			"text"
												lowercase__							: Optional[Any]=			load_dataset(UpperCamelCase__   ,						data_files=UpperCamelCase__		)
						# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
						# https://huggingface.co/docs/datasets/loading_datasets.html.

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.
						lowercase__							: Union[str, Any]=			{
						    "cache_dir": model_args.cache_dir,
						    "revision": model_args.model_revision,
						    "use_auth_token": True if model_args.use_auth_token else None,
						}
						if model_args.config_name:
												lowercase__							: Tuple=			AutoConfig.from_pretrained(model_args.config_name   ,						**UpperCamelCase__		)
						elif model_args.model_name_or_path:
												lowercase__							: Optional[int]=			AutoConfig.from_pretrained(model_args.model_name_or_path   ,						**UpperCamelCase__		)
						else:
												lowercase__							: int=			CONFIG_MAPPING[model_args.model_type]()
												logger.warning("You are instantiating a new config instance from scratch."		)
												if model_args.config_overrides is not None:
																		logger.info(f'''Overriding config: {model_args.config_overrides}'''		)
																		config.update_from_string(model_args.config_overrides		)
																		logger.info(f'''New config: {config}'''		)

						lowercase__							: List[str]=			{
						    "cache_dir": model_args.cache_dir,
						    "use_fast": model_args.use_fast_tokenizer,
						    "revision": model_args.model_revision,
						    "use_auth_token": True if model_args.use_auth_token else None,
						}
						if model_args.tokenizer_name:
												lowercase__							: Optional[Any]=			AutoTokenizer.from_pretrained(model_args.tokenizer_name   ,						**UpperCamelCase__		)
						elif model_args.model_name_or_path:
												lowercase__							: List[Any]=			AutoTokenizer.from_pretrained(model_args.model_name_or_path   ,						**UpperCamelCase__		)
						else:
												raise ValueError(
												    "You are instantiating a new tokenizer from scratch. This is not supported by this script."
												    "You can do it from another script, save it, and load it from here, using --tokenizer_name."		)

						if model_args.model_name_or_path:
												lowercase__							: str=			AutoModelForMaskedLM.from_pretrained(
												    model_args.model_name_or_path   ,						from_tf=bool(".ckpt" in model_args.model_name_or_path		)   ,						config=UpperCamelCase__   ,						cache_dir=model_args.cache_dir   ,						revision=model_args.model_revision   ,						use_auth_token=True if model_args.use_auth_token else None   ,						)
						else:
												logger.info("Training new model from scratch"		)
												lowercase__							: int=			AutoModelForMaskedLM.from_config(UpperCamelCase__		)

						model.resize_token_embeddings(len(UpperCamelCase__		)		)

						# Preprocessing the datasets.
						# First we tokenize all the texts.
						if training_args.do_train:
												lowercase__							: Any=			datasets["train"].column_names
						else:
												lowercase__							: int=			datasets["validation"].column_names
						lowercase__							: Dict=			"text" if "text" in column_names else column_names[0]

						lowercase__							: List[Any]=			"max_length" if data_args.pad_to_max_length else False

						def tokenize_function(A		):
												# Remove empty lines
												lowercase__							: int=			[line for line in examples["text"] if len(UpperCamelCase__		) > 0 and not line.isspace()]
												return tokenizer(examples["text"]   ,						padding=UpperCamelCase__   ,						truncation=UpperCamelCase__   ,						max_length=data_args.max_seq_length		)

						lowercase__							: Any=			datasets.map(
						    UpperCamelCase__   ,						batched=UpperCamelCase__   ,						num_proc=data_args.preprocessing_num_workers   ,						remove_columns=[text_column_name]   ,						load_from_cache_file=not data_args.overwrite_cache   ,						)

						# Add the chinese references if provided
						if data_args.train_ref_file is not None:
												lowercase__							: List[Any]=			add_chinese_references(tokenized_datasets["train"]   ,						data_args.train_ref_file		)
						if data_args.validation_ref_file is not None:
												lowercase__							: Dict=			add_chinese_references(
												    tokenized_datasets["validation"]   ,						data_args.validation_ref_file		)
						# If we have ref files, need to avoid it removed by trainer
						lowercase__							: List[str]=			data_args.train_ref_file or data_args.validation_ref_file
						if has_ref:
												lowercase__							: Tuple=			False

						# Data collator
						# This one will take care of randomly masking the tokens.
						lowercase__							: int=			DataCollatorForWholeWordMask(tokenizer=UpperCamelCase__   ,						mlm_probability=data_args.mlm_probability		)

						# Initialize our Trainer
						lowercase__							: str=			Trainer(
						    model=UpperCamelCase__   ,						args=UpperCamelCase__   ,						train_dataset=tokenized_datasets["train"] if training_args.do_train else None   ,						eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None   ,						tokenizer=UpperCamelCase__   ,						data_collator=UpperCamelCase__   ,						)

						# Training
						if training_args.do_train:
												if last_checkpoint is not None:
																		lowercase__							: Optional[int]=			last_checkpoint
												elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		):
																		lowercase__							: Any=			model_args.model_name_or_path
												else:
																		lowercase__							: Any=			None
												lowercase__							: List[Any]=			trainer.train(resume_from_checkpoint=UpperCamelCase__		)
												trainer.save_model()  # Saves the tokenizer too for easy upload

												lowercase__							: int=			os.path.join(training_args.output_dir   ,						"train_results.txt"		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase__   ,						"w"		) as writer:
																								logger.info("***** Train results *****"		)
																								for key, value in sorted(train_result.metrics.items()		):
																														logger.info(f'''  {key} = {value}'''		)
																														writer.write(f'''{key} = {value}\n'''		)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																		trainer.state.save_to_json(os.path.join(training_args.output_dir   ,						"trainer_state.json"		)		)

    # Evaluation
						lowercase__							: Union[str, Any]=			{}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"		)

												lowercase__							: int=			trainer.evaluate()

												lowercase__							: int=			math.exp(eval_output["eval_loss"]		)
												lowercase__							: List[Any]=			perplexity

												lowercase__							: Optional[int]=			os.path.join(training_args.output_dir   ,						"eval_results_mlm_wwm.txt"		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase__   ,						"w"		) as writer:
																								logger.info("***** Eval results *****"		)
																								for key, value in sorted(results.items()		):
																														logger.info(f'''  {key} = {value}'''		)
																														writer.write(f'''{key} = {value}\n'''		)

						return results





def 		lowercase__(A		)							->Union[str, Any]:




						"""simple docstring"""



						main()


if __name__ == "__main__":
			main()







'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       bool:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    if len(UpperCamelCase__							) == 1:
        return True
    __lowerCamelCase            =    series[1] - series[0]
    for index in range(len(UpperCamelCase__							) - 1							):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    __lowerCamelCase            =    0
    for val in series:
        answer += val
    return answer / len(UpperCamelCase__							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
